模型介绍
中文整理通义千问QVQ-72B-Preview-GPTQ-Int4
原始模型 Qwen/QVQ-72B-Preview
【模型更新日期】
注：通过 snapshot download 函数传入 revision=... 来下载指定的 tag 版本
【模型列表】
tag 文件大小 最近更新时间
- -------- -------- --------------
g128 41GB 2024-12-31
如果启动时候遇到 gptq marlin 报错，需要强行指定 --quantization gptq。
【修复内容】
- 对GPTQ量化的校准做了额外优化；减少模型的 1.乱吐字 、 2.无限循环 、 3.长文能力丢失 等情况。
- 根据模型实际情况，可以支持1卡、2卡及4卡的 tensor-parallel-size 启动。
【介绍】
QVQ-72B-Preview** is an experimental research model developed by the Qwen team, focusing on enhancing visual reasoning capabilities.
Performance
- *QVQ-72B-Preview** o1-2024-12-17 gpt-4o-2024-05-13 Claude3.5 Sonnet-20241022 Qwen2VL-72B
- ----------------- --------------------- --------------- ------------------- --------------------------- -------------
MMMU(val) 70.3 77.3 69.1 70.4 64.5
MathVista(mini) 71.4 71.0 63.8 65.3 70.5
MathVision(full) 35.9 – 30.4 35.6 25.9
OlympiadBench 20.4 – 25.9 – 11.2
QVQ-72B-Preview** has achieved remarkable performance on various benchmarks. It scored a remarkable 70.3% on the Multimodal Massive Multi-task Understanding (MMMU) benchmark, showcasing QVQ's powerful ability in multidisciplinary understanding and reasoning. Furthermore, the significant improvements on MathVision highlight the model's progress in mathematical reasoning tasks. OlympiadBench also demonstrates the model's enhanced ability to tackle challenging problems.
But It's Not All Perfect: Acknowledging the Limitations***
While **QVQ-72B-Preview** exhibits promising performance that surpasses expectations, it’s important to acknowledge several limitations:
- **Language Mixing and Code-Switching:** The model might occasionally mix different languages or unexpectedly switch between them, potentially affecting the clarity of its responses.
- **Recursive Reasoning Loops:** There's a risk of the model getting caught in recursive reasoning loops, leading to lengthy responses that may not even arrive at a final answer.
- **Safety and Ethical Considerations:** Robust safety measures are needed to ensure reliable and safe performance. Users should exercise caution when deploying this model.
- **Performance and Benchmark Limitations:** Despite the improvements in visual reasoning, QVQ doesn’t entirely replace the capabilities of Qwen2-VL-72B. During multi-step visual reasoning, the model might gradually lose focus on the image content, leading to hallucinations. Moreover, QVQ doesn’t show significant improvement over Qwen2-VL-72B in basic recognition tasks like identifying people, animals, or plants.
Note: Currently, the model only supports single-round dialogues and image outputs. It does not support video inputs.
【模型下载】
【高并发RESTFul API推理】
方式1：vllm
优先采用原作者提供的中文模型卡片；仅有英文说明时由闲社AI翻译整理。重要参数、许可证和商用范围请以原文为准。
你将获得什么
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使用前须知
运行模型需要的设备、工具与依赖，以原作者说明为准。
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